Geometry Preserving Multi-task Metric Learning

doi:10.1007/978-3-642-33460-3_47

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 模式分析与学习

	Geometry Preserving Multi-task Metric Learning
	Yang, Peipei; Huang, Kaizhu; Liu, Cheng-Lin
	2012-09
会议名称	European Conference on Machine Learning
会议录名称	Machine Learning and Knowledge Discovery in Databases
会议日期	2012-9-23
会议地点	Bristol, UK
摘要	Multi-task learning has been widely studied in machine learning due to its capability to improve the performance of multiple related learning problems. However, few researchers have applied it on the important metric learning problem. In this paper, we propose to couple multiple related metric learning tasks with von Neumann divergence. On one hand, the novel regularized approach extends previous methods from the vector regularization to a general matrix regularization framework; on the other hand and more importantly, by exploiting von Neumann divergence as the regularizer, the new multi-task metric learning has the capability to well preserve the data geometry. This leads to more appropriate propagation of side-information among tasks and provides potential for further improving the performance. We propose the concept of geometry preserving probability (PG) and show that our framework leads to a larger PG in theory. In addition, our formulation proves to be jointly convex and the global optimal solution can be guaranteed. A series of experiments across very different disciplines verify that our proposed algorithm can consistently outperform the current methods.
关键词	Multi-task Learning Metric Learning Geometry Preserving
DOI	10.1007/978-3-642-33460-3_47
引用统计
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12500
专题	多模态人工智能系统全国重点实验室_模式分析与学习
通讯作者	Huang, Kaizhu
作者单位	National Laboratory of Pattern Recognition
推荐引用方式 GB/T 7714	Yang, Peipei,Huang, Kaizhu,Liu, Cheng-Lin. Geometry Preserving Multi-task Metric Learning[C],2012.

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